TED2016
Anthony Goldbloom: The jobs we'll lose to machines -- and the ones we won't
Anthony Goldbloom: Službe, ki jih bodo prevzele naprave - in tiste, ki jih ne bodo
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Strojno učenje ni več samo za lahke naloge kot oceniti kreditno tveganje in sortiranje pošte - danes so sposobne veliko bolj kompleksnih stvari, kot je ocenjevanje esejev in diagnoze bolezni. S tem napredkom pa pride tudi neprijetno vprašanje: Bo robot v prihodnosti opravljal tvoje delo?
Anthony Goldbloom - Machine learning expert
Anthony Goldbloom crowdsources solutions to difficult problems using machine learning. Full bio
Anthony Goldbloom crowdsources solutions to difficult problems using machine learning. Full bio
Double-click the English transcript below to play the video.
00:12
So this is my niece.
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To je moja nečakinja.
00:14
Her name is Yahli.
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Ime ji je Yahli.
00:16
She is nine months old.
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Stara je devet mesecev.
00:18
Her mum is a doctor,
and her dad is a lawyer.
and her dad is a lawyer.
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Njena mama je zdravnica
in njen oče je odvetnik.
in njen oče je odvetnik.
00:21
By the time Yahli goes to college,
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Ko bo šla Yahli na fakulteto,
00:23
the jobs her parents do
are going to look dramatically different.
are going to look dramatically different.
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bodo poklici, ki jih opravljata
njena starša, izgledali zelo drugače.
njena starša, izgledali zelo drugače.
00:27
In 2013, researchers at Oxford University
did a study on the future of work.
did a study on the future of work.
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2013 so raziskovalci Oxfordske univerze
naredili študijo o prihodnosti dela.
naredili študijo o prihodnosti dela.
00:32
They concluded that almost one
in every two jobs have a high risk
in every two jobs have a high risk
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Ugotovili so, da ima skoraj ena
od dveh služb visoko tveganje,
od dveh služb visoko tveganje,
00:36
of being automated by machines.
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da jo zamenja stroj.
00:40
Machine learning is the technology
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Strojno učenje je tehnologija,
ki je najbolj zaslužna za to motnjo.
00:42
that's responsible for most
of this disruption.
of this disruption.
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00:44
It's the most powerful branch
of artificial intelligence.
of artificial intelligence.
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Je najbolj uspešna veja
umetne inteligence.
umetne inteligence.
00:47
It allows machines to learn from data
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Strojem omogoča, da se učijo iz podatkov
00:49
and mimic some of the things
that humans can do.
that humans can do.
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in posnemajo stvari,
ki jih ljudje lahko počnejo.
ki jih ljudje lahko počnejo.
00:51
My company, Kaggle, operates
on the cutting edge of machine learning.
on the cutting edge of machine learning.
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Moje podjetje, Kaggle,
je najbolj napredno v strojnem učenju.
je najbolj napredno v strojnem učenju.
00:55
We bring together
hundreds of thousands of experts
hundreds of thousands of experts
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Združujemo sto tisoče strokovnjakov,
00:57
to solve important problems
for industry and academia.
for industry and academia.
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da rešujejo pomembne probleme
za industrijo in znanost.
za industrijo in znanost.
01:01
This gives us a unique perspective
on what machines can do,
on what machines can do,
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To nam daje edinstveno perspektivo
o tem, kaj stroji zmorejo,
o tem, kaj stroji zmorejo,
01:04
what they can't do
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česa ne,
01:05
and what jobs they might
automate or threaten.
automate or threaten.
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in katere službe bodo
avtomatizirali ali ogrozili.
avtomatizirali ali ogrozili.
01:09
Machine learning started making its way
into industry in the early '90s.
into industry in the early '90s.
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Strojno učenje je začelo prodirati
v industrijo v zgodnjih 90-ih.
v industrijo v zgodnjih 90-ih.
01:12
It started with relatively simple tasks.
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Začelo se je z relativno
preprostimi nalogami.
preprostimi nalogami.
01:15
It started with things like assessing
credit risk from loan applications,
credit risk from loan applications,
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Začelo se je z ocenjevanjem
tveganja pri prosilcih za kredite,
tveganja pri prosilcih za kredite,
01:19
sorting the mail by reading
handwritten characters from zip codes.
handwritten characters from zip codes.
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sortiranje pošte, tako da so brali
na roke napisane poštne številke.
na roke napisane poštne številke.
01:24
Over the past few years, we have made
dramatic breakthroughs.
dramatic breakthroughs.
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V zadnjih nekaj letih smo naredili
nekaj dramatičnih prebojev.
nekaj dramatičnih prebojev.
01:27
Machine learning is now capable
of far, far more complex tasks.
of far, far more complex tasks.
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Strojno učenje je sedaj sposobno
veliko, veliko bolj kompleksnih nalog.
veliko, veliko bolj kompleksnih nalog.
01:31
In 2012, Kaggle challenged its community
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Leta 2012 je Kaggle izzval svojo skupnost,
01:35
to build an algorithm
that could grade high-school essays.
that could grade high-school essays.
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naj zgradi algoritem, ki bo lahko
ocenjeval srednješolske eseje.
ocenjeval srednješolske eseje.
01:38
The winning algorithms
were able to match the grades
were able to match the grades
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Ocene zmagovalnih algoritmov so se ujemale
01:40
given by human teachers.
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s tistimi, ki so jih dali učitelji.
01:43
Last year, we issued
an even more difficult challenge.
an even more difficult challenge.
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Lani smo dali še težji izziv.
01:46
Can you take images of the eye
and diagnose an eye disease
and diagnose an eye disease
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Lahko slikaš oko
in diagnosticiraš očesno bolezen,
in diagnosticiraš očesno bolezen,
01:49
called diabetic retinopathy?
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imenovano diabetična retinopatija?
01:51
Again, the winning algorithms
were able to match the diagnoses
were able to match the diagnoses
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Spet so se diagnoze najboljših
algoritmov ujemale
algoritmov ujemale
01:55
given by human ophthalmologists.
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z diagnozami oftalmologov.
01:57
Now, given the right data,
machines are going to outperform humans
machines are going to outperform humans
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S pravimi podatki bi lahko
bili stroji boljši od ljudi
bili stroji boljši od ljudi
pri takih nalogah.
02:00
at tasks like this.
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02:01
A teacher might read 10,000 essays
over a 40-year career.
over a 40-year career.
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Učitelj, v svoji 40-letni karieri
prebere morda 10.000 esejev.
prebere morda 10.000 esejev.
02:06
An ophthalmologist might see 50,000 eyes.
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Oftalmolog vidi 50.000 oči.
02:08
A machine can read millions of essays
or see millions of eyes
or see millions of eyes
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Stroj lahko prebere na milijone
esejev ali vidi milijone oči
esejev ali vidi milijone oči
02:12
within minutes.
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v nekaj minutah.
02:14
We have no chance of competing
against machines
against machines
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Ne moremo tekmovati s stroji
02:17
on frequent, high-volume tasks.
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na pogostih, obsežnih nalogah.
02:20
But there are things we can do
that machines can't do.
that machines can't do.
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A mi lahko počnemo stvari,
ki jih stroji ne morejo.
ki jih stroji ne morejo.
Področje, na katerem so stroji
le malo napredovali,
le malo napredovali,
02:24
Where machines have made
very little progress
very little progress
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je obvladovanje novih situacij.
02:27
is in tackling novel situations.
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02:28
They can't handle things
they haven't seen many times before.
they haven't seen many times before.
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Ne obvladajo stvari,
ki jih niso videli že večkrat.
ki jih niso videli že večkrat.
02:33
The fundamental limitations
of machine learning
of machine learning
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Osnovna omejitev strojnega učenja
02:35
is that it needs to learn
from large volumes of past data.
from large volumes of past data.
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je, da se mora učiti iz velike
količine preteklih podatkov.
količine preteklih podatkov.
02:39
Now, humans don't.
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Tega ljudem ni treba.
02:41
We have the ability to connect
seemingly disparate threads
seemingly disparate threads
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Imamo sposobnost, da povežemo
na videz različne konce,
na videz različne konce,
02:44
to solve problems we've never seen before.
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da rešimo nove probleme.
02:46
Percy Spencer was a physicist
working on radar during World War II,
working on radar during World War II,
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Percy Spencer je bil fizik, ki je delal
na radarju med drugo svetovno vojno,
na radarju med drugo svetovno vojno,
02:51
when he noticed the magnetron
was melting his chocolate bar.
was melting his chocolate bar.
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ko je opazil, da magnetron
topi njegovo čokoladico.
topi njegovo čokoladico.
02:54
He was able to connect his understanding
of electromagnetic radiation
of electromagnetic radiation
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Zmožen je bil povezati svoje znanje
o elektromagnetni radiaciji
o elektromagnetni radiaciji
02:58
with his knowledge of cooking
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s svojim znanjem o kuhanju,
02:59
in order to invent -- any guesses? --
the microwave oven.
the microwave oven.
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da je izumil - uganete kaj?
- mikrovalovno pečico.
- mikrovalovno pečico.
03:03
Now, this is a particularly remarkable
example of creativity.
example of creativity.
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No, to je res neverjeten
primer ustvarjalnosti.
primer ustvarjalnosti.
03:06
But this sort of cross-pollination
happens for each of us in small ways
happens for each of us in small ways
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A tako navzkrižno opraševanje
se dogaja vsakemu od nas po malem
se dogaja vsakemu od nas po malem
03:10
thousands of times per day.
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tisočkrat na dan.
03:12
Machines cannot compete with us
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Stroji z nami ne morejo tekmovati,
03:14
when it comes to tackling
novel situations,
novel situations,
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ko pride do ubadanja z novimi situacijami
03:16
and this puts a fundamental limit
on the human tasks
on the human tasks
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in to da temeljno omejitev
na človeške naloge,
na človeške naloge,
03:19
that machines will automate.
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ki jih bodo stroji avtomatizirali.
03:22
So what does this mean
for the future of work?
for the future of work?
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Kaj torej to pomeni za prihodnost dela?
03:24
The future state of any single job lies
in the answer to a single question:
in the answer to a single question:
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Prihodnost katerekoli službe leži
v odgovoru na eno vprašanje:
v odgovoru na eno vprašanje:
03:29
To what extent is that job reducible
to frequent, high-volume tasks,
to frequent, high-volume tasks,
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do katere mere lahko to službo zreduciramo
na pogoste, obsežne naloge
na pogoste, obsežne naloge
03:34
and to what extent does it involve
tackling novel situations?
tackling novel situations?
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in do katere mere vsebuje
obvladovanje novih situacij?
obvladovanje novih situacij?
03:37
On frequent, high-volume tasks,
machines are getting smarter and smarter.
machines are getting smarter and smarter.
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Na pogostih, obsežnih nalogah
stroji postajajo vse pametnejši.
stroji postajajo vse pametnejši.
03:42
Today they grade essays.
They diagnose certain diseases.
They diagnose certain diseases.
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Danes ocenjujejo eseje.
Diagnosticirajo določene bolezni.
Diagnosticirajo določene bolezni.
03:44
Over coming years,
they're going to conduct our audits,
they're going to conduct our audits,
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Čez leta bodo delali revizije
03:47
and they're going to read boilerplate
from legal contracts.
from legal contracts.
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in brali šablonska besedila na pogodbah.
Računovodje in odvetnike še potrebujemo.
03:50
Accountants and lawyers are still needed.
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Potrebni bodo za kompleksno
davčno strukturiranje,
davčno strukturiranje,
03:52
They're going to be needed
for complex tax structuring,
for complex tax structuring,
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za prelomne sodne postopke.
03:55
for pathbreaking litigation.
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A stroji bodo zožili njihove vrste
in težje bo dobiti te službe.
in težje bo dobiti te službe.
03:57
But machines will shrink their ranks
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03:58
and make these jobs harder to come by.
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04:00
Now, as mentioned,
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Kot sem omenil, stroji
pri novih situacijah ne napredujejo.
pri novih situacijah ne napredujejo.
04:01
machines are not making progress
on novel situations.
on novel situations.
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04:04
The copy behind a marketing campaign
needs to grab consumers' attention.
needs to grab consumers' attention.
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Reklamno besedilo za marketinško
kampanjo mora pritegniti potrošnika.
kampanjo mora pritegniti potrošnika.
Izstopati mora iz množice.
04:08
It has to stand out from the crowd.
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Poslovna strategija je iskanje tržnih niš,
04:10
Business strategy means
finding gaps in the market,
finding gaps in the market,
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stvari, ki jih nihče drug ne dela.
04:12
things that nobody else is doing.
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04:14
It will be humans that are creating
the copy behind our marketing campaigns,
the copy behind our marketing campaigns,
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Ljudje bodo ustvarjali reklamna besedila
v marketinških kampanjah
v marketinških kampanjah
04:18
and it will be humans that are developing
our business strategy.
our business strategy.
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in ljudje bodo razvijali
naše poslovne strategije.
naše poslovne strategije.
04:21
So Yahli, whatever you decide to do,
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Yahli, karkoli se odločiš početi,
04:24
let every day bring you a new challenge.
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naj ti vsak dan prinese nov izziv.
04:27
If it does, then you will stay
ahead of the machines.
ahead of the machines.
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Če ti bo, boš imela prednost pred stroji.
04:31
Thank you.
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Hvala.
04:32
(Applause)
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(Aplavz)
ABOUT THE SPEAKER
Anthony Goldbloom - Machine learning expertAnthony Goldbloom crowdsources solutions to difficult problems using machine learning.
Why you should listen
Anthony Goldbloom is the co-founder and CEO of Kaggle. Kaggle hosts machine learning competitions, where data scientists download data and upload solutions to difficult problems. Kaggle has a community of over 600,000 data scientists and has worked with companies ranging Facebook to GE on problems ranging from predicting friendships to flight arrival times.
Before Kaggle, Anthony worked as an econometrician at the Reserve Bank of Australia, and before that the Australian Treasury. In 2011 and 2012, Forbes named Anthony one of the 30 under 30 in technology; in 2013 the MIT Tech Review named him one of top 35 innovators under the age of 35, and the University of Melbourne awarded him an Alumni of Distinction Award. He holds a first call honors degree in Econometrics from the University of Melbourne.
Anthony Goldbloom | Speaker | TED.com